batching

Derive the weight-only shortcut, then add KV traffic and feasible eight-GPU placement. A worked latency floor shows why batch size alone cannot establish the decode bottleneck.

Preserve document identity through packed prompts and completion reordering. Derive rounded cache reservations, then measure feasible wave sizes against the API contract.

Follow adapter-specific token rows through a shared base, calculate stored adapter bytes, and separate the resident set from the active batch.

Images add preprocessing and encoder work before the language model can answer. Their effect on prompt length and cache memory depends on the model. Budget each stage, and distinguish image-embedding reuse from prefix-cache reuse.

Work two audio timelines: input accumulation before transcription and playback starvation after synthesis. Compare real-time factor, response delay and safe concurrent capacity without mixing their units.

Follow media through fetching, preprocessing, encoding and language-model execution. Derive visual positions from the actual processor output, account for encoder placement and separate the caches and latency stages that text-only monitoring misses.

Measure exposed launch and dispatch delay, actual graph coverage, capture memory and warmup. Batch size influences the result, but does not alone determine whether graphs improve the service.

Group compatible adapters by pinned base, derive their size from target matrices and ranks, and benchmark mixed batches. Catalogue size, CPU residency, GPU slots and active adapters are separate limits; base sharing does not make fifty variants operationally free.

Follow a video job from prompt to downloadable artifact. Work the latent-shape arithmetic, compare batching with actual delivery times, and design bounded queues, cancellation and safe retries.

Compare the output of a speculative round with drafting and verification cost. Work the break-even condition, then design a measured rollout with memory and correctness checks.

Implement size- and timer-triggered batching, then test cancellation, handler errors and the latency added before dispatch.

Derive and simulate speculative output counts, then compare them with explicitly assumed or measured round costs.